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An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks

In Cognitive Radio (CR), the conventional narrow band spectrum sensing requires either random channel sensing order or predefined channel sensing sequence to sense all channels in a specified spectrum band in order to detect vacant channels. This may be inefficient in energy constraint devices netwo...

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Main Authors: Mustapha, Ibrahim, Mohd Ali, Borhanuddin, Sali, Aduwati, A. Rasid, Mohd Fadlee, Mohamad, Hafizal
Format: Article
Language:English
Published: Elsevier BV 2017
Online Access:http://psasir.upm.edu.my/id/eprint/60814/1/An%20energy%20efficient%20reinforcement%20learning%20based%20cooperative%20channel%20sensing%20for%20cognitive%20radio%20sensor%20networks.pdf
http://psasir.upm.edu.my/id/eprint/60814/
https://reader.elsevier.com/reader/sd/pii/S1574119216301079?token=87F00C23B2C2F3735899E0708B00FBCA183821B3CCBE30C828B52A501BC02A514652E9DB93A83ECEB82D24B69145D631
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spelling my.upm.eprints.608142019-04-24T11:13:18Z http://psasir.upm.edu.my/id/eprint/60814/ An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks Mustapha, Ibrahim Mohd Ali, Borhanuddin Sali, Aduwati A. Rasid, Mohd Fadlee Mohamad, Hafizal In Cognitive Radio (CR), the conventional narrow band spectrum sensing requires either random channel sensing order or predefined channel sensing sequence to sense all channels in a specified spectrum band in order to detect vacant channels. This may be inefficient in energy constraint devices networks such as Cognitive Radio Wireless Sensor Network (CR-WSN). In this paper, we propose a Reinforcement Learning based clustered Cooperative Channel Sensing (RL-CCS) that learns channels’ dynamic behaviors in terms of channel availability, sensing energy cost, and channel impairment to achieve optimal sensing sequence and optimal set of channels. The problem of selecting optimal policy is formulated as a Markov Decision Problem (MDP) to determine optimal solutions that minimize sensing energy while improving Primary User (PU) detection and channel utilization in CR-WSN. Simulation results show convergence and adaptability of the algorithm to dynamic environment in achieving optimal solutions. The results also indicate that with optimal channel sensing sequence and optimal sets of channels, sensing energy cost savings of 15.14% per channel sensing cycle can be achieved while improving PU detection accuracy and channel utilization compared to the sensing sequence based on Greedy search approach. Performance comparison of the proposed algorithm with other benchmark schemes indicates viability of our proposed approach over the other schemes in minimizing sensing energy and improving PU detection performance. Elsevier BV 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/60814/1/An%20energy%20efficient%20reinforcement%20learning%20based%20cooperative%20channel%20sensing%20for%20cognitive%20radio%20sensor%20networks.pdf Mustapha, Ibrahim and Mohd Ali, Borhanuddin and Sali, Aduwati and A. Rasid, Mohd Fadlee and Mohamad, Hafizal (2017) An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks. Pervasive and Mobile Computing, 35. 165 - 184. ISSN 1574-1192; ESSN: 1873-1589 https://reader.elsevier.com/reader/sd/pii/S1574119216301079?token=87F00C23B2C2F3735899E0708B00FBCA183821B3CCBE30C828B52A501BC02A514652E9DB93A83ECEB82D24B69145D631 10.1016/j.pmcj.2016.07.007
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description In Cognitive Radio (CR), the conventional narrow band spectrum sensing requires either random channel sensing order or predefined channel sensing sequence to sense all channels in a specified spectrum band in order to detect vacant channels. This may be inefficient in energy constraint devices networks such as Cognitive Radio Wireless Sensor Network (CR-WSN). In this paper, we propose a Reinforcement Learning based clustered Cooperative Channel Sensing (RL-CCS) that learns channels’ dynamic behaviors in terms of channel availability, sensing energy cost, and channel impairment to achieve optimal sensing sequence and optimal set of channels. The problem of selecting optimal policy is formulated as a Markov Decision Problem (MDP) to determine optimal solutions that minimize sensing energy while improving Primary User (PU) detection and channel utilization in CR-WSN. Simulation results show convergence and adaptability of the algorithm to dynamic environment in achieving optimal solutions. The results also indicate that with optimal channel sensing sequence and optimal sets of channels, sensing energy cost savings of 15.14% per channel sensing cycle can be achieved while improving PU detection accuracy and channel utilization compared to the sensing sequence based on Greedy search approach. Performance comparison of the proposed algorithm with other benchmark schemes indicates viability of our proposed approach over the other schemes in minimizing sensing energy and improving PU detection performance.
format Article
author Mustapha, Ibrahim
Mohd Ali, Borhanuddin
Sali, Aduwati
A. Rasid, Mohd Fadlee
Mohamad, Hafizal
spellingShingle Mustapha, Ibrahim
Mohd Ali, Borhanuddin
Sali, Aduwati
A. Rasid, Mohd Fadlee
Mohamad, Hafizal
An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
author_facet Mustapha, Ibrahim
Mohd Ali, Borhanuddin
Sali, Aduwati
A. Rasid, Mohd Fadlee
Mohamad, Hafizal
author_sort Mustapha, Ibrahim
title An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
title_short An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
title_full An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
title_fullStr An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
title_full_unstemmed An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
title_sort energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
publisher Elsevier BV
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/60814/1/An%20energy%20efficient%20reinforcement%20learning%20based%20cooperative%20channel%20sensing%20for%20cognitive%20radio%20sensor%20networks.pdf
http://psasir.upm.edu.my/id/eprint/60814/
https://reader.elsevier.com/reader/sd/pii/S1574119216301079?token=87F00C23B2C2F3735899E0708B00FBCA183821B3CCBE30C828B52A501BC02A514652E9DB93A83ECEB82D24B69145D631
_version_ 1643837445924454400
score 13.211869

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